Association Between Ginkgo Biloba Extract Prescriptions and Dementia Incidence in Outpatients with Mild Cognitive Impairment in Germany: A Retrospective Cohort Study

Background:

Clinical trials have demonstrated a significant effectiveness of Ginkgo biloba therapy versus placebo in patients with dementia.

Objective:

The present study aims to analyze the impact of Ginkgo biloba drug prescriptions on dementia incidence in patients with mild cognitive impairment (MCI) in a real-world setting.

Methods:

This retrospective study was based on the IQVIA Disease Analyzer database and included patients aged 65 or older with a first diagnosis of MCI from January 2000 to December 2019. Each patient was followed for up to 20 years after MCI diagnosis until February 2021. Date of the first diagnosis of dementia or loss to follow-up, whichever occurred first, was noted. To estimate the association between Ginkgo biloba prescriptions during the follow-up and dementia incidence, a multivariable Cox regression analysis was performed, adjusted for age, sex, health insurance, documented co-diagnoses, and prescription of cholinesterase inhibitors.

Results:

Overall, 24,483 MCI patients (mean age: 77.0 years, 56.3% women) were included. It was found that > 2 prescriptions of Ginkgo biloba were significantly associated with a reduced dementia incidence (HR: 0.71 (95% CI: 0.55–0.91), p = 0.007), as compared with no Ginkgo biloba prescription. The effect of receiving > 3 Ginkgo biloba prescriptions was even stronger, with an HR of 0.64 (95% CI: 0.48–0.86), p = 0.003), while for > 4 prescriptions the HR was 0.58 (95% CI: 0.41–0.82) (p = 0.002).

Conclusion:

All-cause dementia incidence decreased with higher numbers of Ginkgo biloba prescriptions in MCI patients.

Matrix metalloproteinase 10 is linked to the risk of progression to dementia of the Alzheimer's type

Alzheimer's disease has a long asymptomatic phase that offers a substantial time window for intervention. Utilizing this window of opportunity will require early diagnostic and prognostic biomarkers to detect Alzheimer's disease pathology at pre-dementia stages, thus allowing identification of patients who will most probably progress to dementia of the Alzheimer's type and benefit from specific disease-modifying therapies. Consequently, we searched for CSF proteins associated with disease progression along with the clinical disease staging. We measured the levels of 184 proteins in CSF samples from 556 subjective cognitive decline and mild cognitive impairment patients from three independent memory clinic longitudinal studies (Spanish ACE, n = 410; German DCN, n = 93; German Mannheim, n = 53). We evaluated the association between protein levels and clinical stage, and the effect of protein levels on the progression from mild cognitive impairment to dementia of the Alzheimer's type. Mild cognitive impairment subjects with increased CSF level of matrix metalloproteinase 10 showed a higher probability of progressing to dementia of the Alzheimer's type and a faster cognitive decline. CSF matrix metalloproteinase 10 increased the prediction accuracy of CSF Aβ42, P-tau181, and T-tau for conversion to dementia of the Alzheimer's type. Including matrix metalloproteinase 10 to the [A/T/(N)] scheme improved considerably the prognostic value in mild cognitive impairment patients with abnormal Aβ42, but normal P-tau181 and T-tau, and in mild cognitive impairment patients with abnormal Aβ42, P-tau181, and T-tau. Matrix metalloproteinase 10 was correlated with age in subjects with normal Aβ42, P-tau181, and T-tau levels. Our findings support the use of CSF matrix metalloproteinase 10 as a prognostic marker for dementia of the Alzheimer's type and its inclusion to the [A/T/(N)] scheme to incorporate pathologic aspects beyond amyloid and tau. CSF level of matrix metalloproteinase 10 may reflect ageing and neuroinflammation.

Don’t forget about tau: the effects of ApoE4 genotype on Alzheimer’s disease cerebrospinal fluid biomarkers in subjects with mild cognitive impairment—data from the Dementia Competence Network

ApoE4, the strongest genetic risk factor for Alzheimer’s disease (AD), has been shown to be associated with both beta-amyloid (Aβ) and tau pathology, with the strongest evidence for effects on Aβ, while the association between ApoE4 and tau pathology remains inconsistent. This study aimed to investigate the associations between ApoE4 with CSF Aβ42, total tau (t-tau), phospho-tau181 (p-tau), and with the progression of decline in a large cohort of MCI subjects, both progressors to AD and other dementias, as well as non-progressors. We analyzed associations of CSF Aβ42, p-tau and t-tau with ApoE4 allele frequency cross-sectionally and longitudinally over 3 years of follow-up in 195 individuals with a diagnosis of MCI-stable, MCI-AD converters and MCI progressing to other dementias from the German Dementia Competence Network. In the total sample, ApoE4 carriers had lower concentrations of CSF Aβ42, and increased concentrations of t-tau and p-tau compared to non-carriers in a gene dose-dependent manner. Comparisons of these associations stratified by MCI-progression groups showed a significant influence of ApoE4 carriership and diagnostic group on all CSF biomarker levels. The effect of ApoE4 was present in MCI-stable individuals but not in the other groups, with ApoE4 + carriers having decreased CSF Aβ 42 levels, and increased concentration of t-tau and p-tau. Longitudinally, individuals with abnormal t-tau and Aβ42 had a more rapid progression of cognitive and clinical decline, independently of ApoE4 genotype. Overall, our results contribute to an emerging framework in which ApoE4 involves mechanisms associated with both CSF amyloid-β burden and tau aggregation at specific time points in AD pathogenesis.

Harmonizing neuropsychological assessment for mild neurocognitive disorders in Europe

INTRODUCTION

Harmonized neuropsychological assessment for neurocognitive disorders, an international priority for valid and reliable diagnostic procedures, has been achieved only in specific countries or research contexts.

METHODS

To harmonize the assessment of mild cognitive impairment in Europe, a workshop (Geneva, May 2018) convened stakeholders, methodologists, academic, and non-academic clinicians and experts from European, US, and Australian harmonization initiatives.

RESULTS

With formal presentations and thematic working-groups we defined a standard battery consistent with the U.S. Uniform DataSet, version 3, and homogeneous methodology to obtain consistent normative data across tests and languages. Adaptations consist of including two tests specific to typical Alzheimer's disease and behavioral variant frontotemporal dementia. The methodology for harmonized normative data includes consensus definition of cognitively normal controls, classification of confounding factors (age, sex, and education), and calculation of minimum sample sizes.

DISCUSSION

This expert consensus allows harmonizing the diagnosis of neurocognitive disorders across European countries and possibly beyond.

Performance evaluation of automated white matter hyperintensity segmentation algorithms in a multicenter cohort on cognitive impairment and dementia

BACKGROUND

White matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer's disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.

METHODS

We used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).

RESULTS

Across tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice's coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.

CONCLUSION

To conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Soluble TAM receptors sAXL and sTyro3 predict structural and functional protection in Alzheimer's disease

There is an urgent need to improve the understanding of neuroinflammation in Alzheimer's disease (AD). We analyzed cerebrospinal fluid inflammatory biomarker correlations to brain structural volume and longitudinal cognitive outcomes in the DELCODE study and in a validation cohort of the F.ACE Alzheimer Center Barcelona. We investigated whether respective biomarker changes are evident before onset of cognitive impairment. YKL-40; sTREM2; sAXL; sTyro3; MIF; complement factors C1q, C4, and H; ferritin; and ApoE protein were elevated in pre-dementia subjects with pathological levels of tau or other neurodegeneration markers, demonstrating tight interactions between inflammation and accumulating neurodegeneration even before onset of symptoms. Intriguingly, higher levels of ApoE and soluble TAM receptors sAXL and sTyro3 were related to larger brain structure and stable cognitive outcome at follow-up. Our findings indicate a protective mechanism relevant for intervention strategies aiming to regulate neuroinflammation in subjects with no or subjective symptoms but underlying AD pathology profile.

Association of Cholinergic Basal Forebrain Volume and Functional Connectivity with Markers of Inflammatory Response in the Alzheimer’s Disease Spectrum

Background: Inflammation has been described as a key pathogenic event In Alzheimer’s disease (AD), downstream of amyloid and tau pathology. Preclinical and clinical data suggest that the cholinergic basal forebrain may moderate inflammatory response to different pathologies. Objective: To study the association of cholinergic basal forebrain volume and functional connectivity with measures of neuroinflammation in people from the AD spectrum. Methods: We studied 261 cases from the DELCODE cohort, including people with subjective cognitive decline, mild cognitive impairment, AD dementia, first degree relatives, and healthy controls. Using Bayesian ANCOVA, we tested associations of MRI indices of cholinergic basal forebrain volume and functional connectivity with cerebrospinal fluid (CSF) levels of sTREM2 as a marker of microglia activation, and serum levels of complement C3. Using Bayesian elastic net regression, we determined associations between basal forebrain measures and a large inflammation marker panel from CSF and serum. Results: We found anecdotal to moderate evidence in favor of the absence of an effect of basal forebrain volume and functional connectivity on CSF sTREM2 and serum C3 levels both in Aβ 42/ptau-positive and negative cases. Bayesian elastic net regression identified several CSF and serum markers of inflammation that were associated with basal forebrain volume and functional connectivity. The effect sizes were moderate to small. Conclusion: Our data-driven analyses generate the hypothesis that cholinergic basal forebrain may be involved in the neuroinflammation response to Aβ 42 and phospho-tau pathology in people from the AD spectrum. This hypothesis needs to be tested in independent samples.

Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease

Background

Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge.

Methods

We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection.

Results

Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001).

Conclusion

The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00924-2.

A microRNA signature that correlates with cognition and is a target against cognitive decline

While some individuals age without pathological memory impairments, others develop age-associated cognitive diseases. Since changes in cognitive function develop slowly over time in these patients, they are often diagnosed at an advanced stage of molecular pathology, a time point when causative treatments fail. Thus, there is great need for the identification of inexpensive and minimal invasive approaches that could be used for screening with the aim to identify individuals at risk for cognitive decline that can then undergo further diagnostics and eventually stratified therapies. In this study, we use an integrative approach combining the analysis of human data and mechanistic studies in model systems to identify a circulating 3-microRNA signature that reflects key processes linked to neural homeostasis and inform about cognitive status. We furthermore provide evidence that expression changes in this signature represent multiple mechanisms deregulated in the aging and diseased brain and are a suitable target for RNA therapeutics.

Cognitive profiles of patients with mild cognitive impairment due to Alzheimer's versus Parkinson's disease defined using a base rate approach: Implications for neuropsychological assessments

INTRODUCTION

Large studies on cognitive profiles of patients with mild cognitive impairment (MCI) due to Alzheimer's disease (AD-MCI) compared to Parkinson's disease (PD-MCI) are rare.

METHODS

Data from two multicenter cohort studies in AD and PD were merged using a unified base rate approach for the MCI diagnosis. Cognitive profiles were compared using scores derived from the Consortium to Establish a Registry for Alzheimer's Disease battery.

RESULTS

Patients with AD-MCI showed lower standardized scores on all memory test scores and a language test. Patients with PD-MCI showed lower standardized scores in a set-shifting measure as an executive task. A cross-validated logistic regression with test scores as predictors was able to classify 72% of patients correctly to AD-MCI versus PD-MCI.

DISCUSSION

The applied test battery successfully discriminated between AD-MCI and PD-MCI. Neuropsychological test batteries in clinical practice should always include a broad spectrum of cognitive domains to capture any cognitive changes.

Presenilin 1 Gene Mutation (M139V) in a German Family with Early-Onset Alzheimer's Disease: A Case Report

OBJECTIVE

This study describes a 44-year-old German male with early-onset Alzheimer's disease as a result of a M139V presenilin 1 mutation. The patient has at least seven affected family members, spanning at least four generations.

METHOD

We performed a complete demographic, genetic, neuropsychological, neuropsychiatric, neuroradiological, and neuropathological characterizations of this patient. The findings were compared with previous reports of patients with the same mutation. Demographic, neuropsychological, neuropsychiatric, neuroradiological, and neuropathological data from several affected members of the patient's family were also addressed.

RESULTS

We describe similarities shared with other cases, including age at onset, rapid disease progression, severe deficits in arithmetic and visuo-constructive abilities with relative preservation of naming skills, and the presence of predominant frontal behavioral symptoms. Differences with respect to previously described cases, including the absence of positive neurological or radiological findings, psychotic symptoms, or a depressive disorder, are also identified and discussed.

CONCLUSIONS

Heterogeneity in symptoms between affected patients from the same or from different families suggests that individual, genetic, or epigenetic factors most likely modulate the phenotype of patients carrying the M139V mutation.

Overcoming barriers to the adoption of locating technologies in dementia care: a multi-stakeholder focus group study

Background

Locating technologies are a subtype of assistive technology that aim to support persons with dementia by helping manage spatial orientation impairments and provide aid to care partners by intervening when necessary. Although a variety of locating devices are commercially available, their adoption has remained low in the past years. Several studies have explored barriers to the adoption of assistive technologies from the perspective of professional stakeholders, but in-depth explorations for locating technologies are sparse. Additionally, the inputs of business professionals are lacking. The aim of this study was to expand knowledge on barriers to the adoption of locating technologies from a multi-stakeholder professional perspective, and to explore strategies to optimize adoption.

Methods

In total, 22 professionals working in business (n = 7), healthcare (n = 6) and research (n = 9) fields related to gerontology and gerontechnology participated in our focus group study. Perceptions on the value of using locating technologies for dementia care, barriers to their adoption, as well as salient services and information dissemination strategies were explored. After verbatim transcription, transcripts were analysed following an inductive data-driven content analysis approach in MAXQDA.

Results

Six key adoption barriers centering on: (1) awareness-, (2) technological-, (3) product characteristic- and (4) capital investment-based limitations, (5) unclear benefits, as well as (6) ethical concerns emerged. The interplay between barriers was high. Five core themes on services and information dissemination strategies centering on: (1) digital autonomy support, (2) emergency support, (3) information dissemination actors, (4) product acquisition, and (5) product advertising were extracted.

Conclusions

Our study with interdisciplinary stakeholders expands knowledge on barriers to the adoption of locating technologies for dementia care, and reinforces recommendations that an interdisciplinary strategy is needed to optimize adoption. Also, our findings show that focusing on services to increase digital autonomy and on information dissemination strategies has been largely overlooked and may be particularly effective.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02323-6.

Mediterranean Diet, Alzheimer Disease Biomarkers and Brain Atrophy in Old Age

OBJECTIVE

To determine whether following a Mediterranean-like diet (MeDi) relates to cognitive functions and in vivo biomarkers for Alzheimer disease (AD), we analyzed cross-sectional data from the German DZNE-Longitudinal Cognitive Impairment and Dementia Study. METHOD: The sample (n=512, mean age: 69.5±5.9 years) included 169 cognitively normal participants and subjects at higher AD risk (53 with relatives with AD, 209 with subjective cognitive decline, and 81 with mild cognitive impairment). We defined MeDi adherence based on the Food Frequency Questionnaire. Brain volume outcomes were generated via voxel-based morphometry on T1-MRI and cognitive performance with an extensive neuropsychological battery. AD-related biomarkers (Aβ42/40 ratio, pTau181) in cerebrospinal fluid were assessed in n=226 individuals. We analyzed the associations between MeDi and the outcomes with linear regression models controlling for several covariates. Additionally, we applied hypothesis-driven mediation and moderation analysis.

RESULTS

Higher MeDi adherence related to larger mediotemporal gray matter volume (p<0.05 FWE corrected), better memory (β±SE = 0.03 ± 0.02; p=0.038), and less amyloid (Aβ42/40 ratio, β±SE = 0.003 ± 0.001; p=0.008) and pTau181 pathology (β±SE = -1.96±0.68; p=0.004). Mediotemporal volume mediated the association between MeDi and memory (40% indirect mediation). Finally, MeDi favorably moderated the associations between Aβ42/40 ratio, pTau181 and mediotemporal atrophy. Results were consistent correcting for ApoE-ε4 status.

CONCLUSION

Our findings corroborate the view of MeDi as a protective factor against memory decline and mediotemporal atrophy. Importantly, they suggest that these associations might be explained by a decrease of amyloidosis and tau-pathology. Longitudinal and dietary intervention studies should further examine this conjecture and its treatment implications.

Common variants in Alzheimer's disease and risk stratification by polygenic risk scores

Genetic discoveries of Alzheimer's disease are the drivers of our understanding, and together with polygenetic risk stratification can contribute towards planning of feasible and efficient preventive and curative clinical trials. We first perform a large genetic association study by merging all available case-control datasets and by-proxy study results (discovery n = 409,435 and validation size n = 58,190). Here, we add six variants associated with Alzheimer's disease risk (near APP, CHRNE, PRKD3/NDUFAF7, PLCG2 and two exonic variants in the SHARPIN gene). Assessment of the polygenic risk score and stratifying by APOE reveal a 4 to 5.5 years difference in median age at onset of Alzheimer's disease patients in APOE ɛ4 carriers. Because of this study, the underlying mechanisms of APP can be studied to refine the amyloid cascade and the polygenic risk score provides a tool to select individuals at high risk of Alzheimer's disease.

Resting-State Network Alterations Differ between Alzheimer's Disease Atrophy Subtypes

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.